75 research outputs found
Adsorption of Phosphate from Aqueous Solution Using an Iron-Zirconium Binary Oxide Sorbent
In this study, an iron-zirconium binary oxide with a molar ratio of 4:1 was synthesized by a simple coprecipitation process for removal of phosphate from water. The effects of contact time, initial concentration of phosphate solution, temperature, pH of solution, and ionic strength on the efficiency of phosphate removal were investigated. The adsorption data fitted well to the Langmuir model with the maximum P adsorption capacity estimated of 24.9 mg P/g at pH 8.5 and 33.4 mg P/g at pH 5.5. The phosphate adsorption was pH dependent, decreasing with an increase in pH value. The presence of Cl-, SO (4) (2-) , and CO (3) (2-) had little adverse effect on phosphate removal. A desorbability of approximately 53 % was observed with 0.5 M NaOH, indicating a relatively strong bonding between the adsorbed PO (4) (3-) and the sorptive sites on the surface of the adsorbent. The phosphate uptake was mainly achieved through the replacement of surface hydroxyl groups by the phosphate species and formation of inner-sphere surface complexes at the water/oxide interface. Due to its relatively high adsorption capacity, high selectivity and low cost, this Fe-Zr binary oxide is a very promising candidate for the removal of phosphate ions from wastewater
Transsion TSUP's speech recognition system for ASRU 2023 MADASR Challenge
This paper presents a speech recognition system developed by the Transsion
Speech Understanding Processing Team (TSUP) for the ASRU 2023 MADASR Challenge.
The system focuses on adapting ASR models for low-resource Indian languages and
covers all four tracks of the challenge. For tracks 1 and 2, the acoustic model
utilized a squeezeformer encoder and bidirectional transformer decoder with
joint CTC-Attention training loss. Additionally, an external KenLM language
model was used during TLG beam search decoding. For tracks 3 and 4, pretrained
IndicWhisper models were employed and finetuned on both the challenge dataset
and publicly available datasets. The whisper beam search decoding was also
modified to support an external KenLM language model, which enabled better
utilization of the additional text provided by the challenge. The proposed
method achieved word error rates (WER) of 24.17%, 24.43%, 15.97%, and 15.97%
for Bengali language in the four tracks, and WER of 19.61%, 19.54%, 15.48%, and
15.48% for Bhojpuri language in the four tracks. These results demonstrate the
effectiveness of the proposed method
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